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Dimiter Toshkov Profile picture
Dimiter Toshkov
@DToshkov
#eu, #migration, public policy, #bigdata (& small), research methods & design, #dataviz, politics, public opinion, #bureaucracy @PA_UniLeiden former@EuropeanUni
Apr 20, 2020 • 7 tweets • 3 min read
What accounts for the variation in the policy responses of European countries to the #coronavirus? Why some states acted faster than others?
Here are some provisional answers:
(1) Government effectiveness is strongly *negatively* correlated with the speed of the policy response (2) Countries with higher aggregate freedom scores have been slower in closing schools and imposing national lockdowns
Apr 13, 2020 • 5 tweets • 3 min read
Mortality in The Netherlands has risen sharply since March 2020.
According to the latest @statistiekcbs numbers, number of deaths (per million of population) in week 14 (March 30 - April 5) is **75% higher** than the average for the period 1995-2019. Taking into account the number of confirmed COVID-19 deaths, the total number of deaths in Week 14 of 2020 is 40% higher than the average of the period 1995-2019 for the same week.
This suggests the actual number of #COVID2019NL deaths is more than double the official count.
Jan 31, 2019 • 5 tweets • 3 min read
Did you ever want to get government positions from party-level data?
You're in luck!
Here is how to use some #rstats functions that calculate party positions & salience from @manifesto_proj data & aggregate these into gov. positions & over time @ParlGov bit.ly/2DMPLuh for the functions to work, you need to get Manifesto Corpus (@manifesto_proj ) & @ParlGov data and follow the tutorial to align these two data sources. It was no fun identifying misaligned election dates and party codes, but now YOU don't have to do this.
Then, it's a breeze.
Nov 19, 2018 • 10 tweets • 5 min read
I recently finished 'The Book of Why' by @yudapearl
I would highly recommend it to everyone interested in causality and scientific explanations. /THREAD
amzn.to/2OW9v0Q
#bookofwhy The book is rather accessible, so you don't need to be a #causalinference nerd to follow the arguments. At the same time, there is a lot to learn even if you have struggled your way through Causality (CUP, 2009) and the technical papers
amzn.to/2OSmzEb